Biological function depends on the interaction of molecules, not on their isolated structures alone. This proposal addresses a basic tenet of structural genomics - the atomic structures of macromolecular complexes will lead to understanding biological function. Knowing, however, the structures of individual molecules, and that they interact, is not sufficient to elucidate the structure of the complex. Structures of complexes are difficult to obtain by X-ray crystallography or NMR spectroscopy. Other experimental methods such as mutagenesis, cross-linking, or electron microscopy by themselves do not provide sufficient information to fully define the atomic structure of a macromolecular complex; nor does computational docking alone. Here, we propose an innovative approach in which improved analysis of mass-spectrometry hydrogen-exchange data is coordinated with computational docking to give the structures of macromolecular complexes. Our proposed technology uses a rapid, well-developed experimental technique, requires only a few hours of computer time, and can elucidate a wide range of macromolecular interactions. Our hypothesis is that combining information about molecular interfaces and about the location of regions of conformational change obtained from hydrogen-deuterium exchange measurements using a mass spectrometer with computational docking will provide useful, experimentally testable predictions of the structure of macromolecular complexes. This project will (1) Develop improved algorithms for extracting and using more complete H/2H exchange data from experiments; (2) Calibrate the current reliability and robustness of our coordinated computational/mass-spectrometry docking method on protein kinase complexes and on thrombin-thrombomodulin complexes; (3) Extend the combined computational/experimental approach to systems that change conformation on binding; (4) Test the approach on biologically important weakly binding systems such as electron transport systems; and (5) Determine the optimal combination of experimental and computational information required for useful, reliable, and testable predictions. [unreadable] [unreadable] [unreadable]